603 results on '"Constrained optimization"'
Search Results
152. Allocating treatment resources for hepatitis C in the UK: a constrained optimization modelling approach.
- Author
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Han, Ru, Liang, Shuyao, François, Clément, Aballea, Samuel, Clay, Emilie, and Toumi, Mondher
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CONSTRAINED optimization ,HEPATITIS C ,CHRONIC hepatitis C ,MEN who have sex with men ,LINEAR programming ,ALLOCATION of organs, tissues, etc. - Abstract
Background and objective: Although the treatment of chronic hepatitis C (CHC) has significantly evolved with the introduction of direct-acting antivirals, the treatment uptake rates have been low especially among marginalized groups in the UK, such as people who inject drug (PWID) and men who have sex with men (MSM). Cutting health inequality is a major focus of healthcare agencies. This study aims to identify the optimal allocation of treatment budget for chronic hepatitis CHC among populations and treatments in the UK so that liver-related mortality in patients with CHC is minimized, given the constraint of treatment budget and equity issue. Methods: A constrained optimization modelling of resource allocation for the treatment of CHC was developed in Excel from the perspective of the UK National Health System over a lifetime horizon. The model was designated with the objective function of minimizing liver-related deaths by varying the decision variables, representing the number of patients receiving each treatment (elbasvir-grazoprevir, ombitasvir-paritaprevir-ritonavir-dasabuvir, sofosbuvir-ledipasvir, and pegylated interferon-ribavirin) in each population (the general population, PWID, and MSM). Two main constraints were formulated including treatment budget and the issue of equity. The model was populated with UK local data applying linear programming and underwent internal and external validation. Scenario analyses were performed to assess the robustness of model results. Results: Within the constraints of no additional funding over original spending in status quo and the consideration of the issue of equity among populations, the optimal allocation from the constrained optimization modelling (treating 13,122 PWID, 160 MSM, and 904 general patients with ombitasvir-paritaprevir-ritonavir-dasabuvir) was found to treat 2,430 more patients (relative change: 20.7%) and avert 78 liver-related deaths (relative change: 0.3%) compared with the current allocation. The number of patients receiving treatment increased 4,928 (relative change: 60.1%) among PWID and 42 (relative change: 35.8%) among MSM. Conclusion: The current allocation of treatment budget for CHC is not optimal in the UK. More patients would be treated, and more liver-related deaths would be avoided using a new allocation from a constrained optimization modelling without incurring additional spending and considering the issue of equity. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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153. Local Modal Frequency Improvement with Optimal Stiffener by Constraints Transformation Method.
- Author
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Chen, Shenyan, Dai, Ziqi, Shi, Wenjing, Liu, Yanjie, and Li, Jianhongyu
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PROBLEM solving ,STRUCTURAL design ,DEAD loads (Mechanics) ,CONSTRAINED optimization ,ARTIFICIAL satellite tracking ,STRUCTURAL dynamics ,STRUCTURAL optimization - Abstract
Local modal vibration could adversely affect the dynamical environment, which should be considered in the structural design. For the mode switching phenomena, the traditional structural optimization method for problems with specific order of modal frequency constraints could not be directly applied to solve problems with local frequency constraints. In the present work, a novel approximation technique without mode tracking is proposed. According to the structural character, three reasonable assumptions, unchanged mass matrix, accordant modal shape, and reversible stiffness matrix, have been used to transform the optimization problem with local frequency constraints into a problem with nodal displacement constraints in the local area. The static load case is created with the modal shape equilibrium forces, then the displacement constrained optimization is relatively easily solved to obtain the optimal design, which satisfies the local frequency constraints as well. A numerical example is used to verify the feasibility of the proposed approximation method. Then, the method is further applied in a satellite structure optimization problem. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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154. Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition.
- Author
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Chen, Zhili, Wang, Peng, Gui, Zhixian, and Mao, Qinghui
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DATABASES ,HYDRAULIC fracturing ,INDUCTIVE effect ,SIGNAL-to-noise ratio ,DATA analysis - Abstract
Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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155. Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria.
- Author
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Strnad, Damjan and Kohek, Štefan
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TREE pruning ,CONSTRAINED optimization ,SOFTWARE development tools ,FRUIT trees - Abstract
Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic pruning optimization with respect to a set of quantitative objectives, which is important for analytical purposes and potential applications in automated pruning. However, the existing studies in pruning optimization focus on a single type of objective, such as light distribution within the crown. In this paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are used to observe the emergence of different pruning patterns in the non-dominated solution sets. We also propose the use of independent constraint objectives as a new mechanism to confine overfitting of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2, and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which use external solution archives, can produce better sets of non-dominated solutions than NSGA-II. [ABSTRACT FROM AUTHOR]
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- 2021
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156. An Efficient Stochastic Constrained Path Planner for Redundant Manipulators.
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Gil Aparicio, Arturo and Valls Miro, Jaime
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CONSTRAINED optimization ,MANIPULATORS (Machinery) ,PLANNERS ,KINEMATICS - Abstract
This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks. [ABSTRACT FROM AUTHOR]
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- 2021
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157. A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition.
- Author
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Otis, Martin J.-D. and Vandewynckel, Julien
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SUBSET selection ,ALGORITHMS ,CONSTRAINED optimization ,GESTURE ,MATHEMATICAL optimization - Abstract
Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%. [ABSTRACT FROM AUTHOR]
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- 2021
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158. A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication.
- Author
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Osman, Radwa Ahmed and Abdelsalam, Ahmed Kadry
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MATHEMATICAL optimization ,AUTONOMOUS vehicles ,EMERGENCY vehicles ,INTELLIGENT transportation systems ,CONSTRAINED optimization ,TRAFFIC congestion - Abstract
Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL). [ABSTRACT FROM AUTHOR]
- Published
- 2021
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159. Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation.
- Author
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Muñoz-Reina, José Saúl, Villarreal-Cervantes, Miguel Gabriel, and Corona-Ramírez, Leonel Germán
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KINEMATIC chains ,REHABILITATION ,CONSTRAINED optimization ,KEY performance indicators (Management) ,METAHEURISTIC algorithms ,MEDICAL care - Abstract
Currently, rehabilitation systems with closed kinematic chain mechanisms are low-cost alternatives for treatment and health care. In designing these systems, the dimensional synthesis is commonly stated as a constrained optimization problem to achieve repetitive rehabilitation movements, and metaheuristic algorithms for constrained problems are promising methods for searching solutions in the complex search space. The Constraint Handling Techniques (CHTs) in metaheuristic algorithms have different capacities to explore and exploit the search space. However, the study of the relationship in the CHT performance of the mechanism dimensional synthesis for rehabilitation systems has not been addressed, resulting in an important gap in the literature of such problems. In this paper, we present a comparative empirical study to investigate the influence of four CHTs (penalty function, feasibility rules, stochastic-ranking, and ϵ -constraint) on the performance of ten representative algorithms that have been reported in the literature for solving mechanism synthesis for rehabilitation (four-bar linkage, eight-bar linkage, and cam-linkage mechanisms). The study involves analysis of the overall performance, six performance metrics, and evaluation of the obtained mechanism. This identified that feasibility rules usually led to efficient optimization for most analyzed algorithms and presented more consistency of the obtained results in these kinds of problems. [ABSTRACT FROM AUTHOR]
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- 2021
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160. Structural Damage Identification Using a Modified Directional Bat Algorithm.
- Author
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Su, Yonghui, Liu, Lijun, and Lei, Ying
- Subjects
SWARM intelligence ,PROBLEM solving ,ALGORITHMS ,BATS ,CONSTRAINED optimization - Abstract
Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat's position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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161. A Soil-Dependent Approach for the Design of Novel Negative Stiffness Seismic Protection Devices.
- Author
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Kapasakalis, Konstantinos A., Antoniadis, Ioannis A., and Sapountzakis, Evangelos J.
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BASE isolation system ,CONSTRAINED optimization ,VIBRATION absorbers ,ACCELEROGRAMS ,RETROFITTING - Abstract
Conventional base isolation (BI) techniques require a great reduction in the fundamental frequency of the system in order to mitigate the structural dynamic responses due to earthquake excitations. However, the resulting base displacements are large and can cause utility connection problems, rendering BI inadequate for retrofitting. This paper proposes a vibration control system (VCS) that can be used as a supplement to the conventional BI to increase the effective damping, and thus reduce the required base displacements. A novel passive negative stiffness (NS)-based vibration absorber, based on the KDamper, is implemented in parallel to a BI. The design of the VCS follows a constrained optimization approach that accounts for geometrical and manufacturing limitations. The NS is realized with a realistic displacement-dependent mechanism that generates controlled NS. The VCS is designed for various soil-types in order to determine its effectiveness and soil-structure-interaction (SSI) effects are accounted with respect to the soil-type. The earthquake excitation input is selected according to the EC8 by generating a database of artificial accelerograms for each ground type. Finally, the VCS is compared to a conventional BI, and based on the numerical results obtained, the VCS is an effective alternative to BI and a possible retrofitting option. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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162. Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach.
- Author
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Bakry, Walid, Rashid, Audil, Al-Mohamad, Somar, and El-Kanj, Nasser
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PORTFOLIO management (Investments) ,BITCOIN ,ASSET allocation ,SHARPE ratio ,CONSTRAINED optimization - Abstract
This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The study employs different constraining optimization frameworks that seek to maximize risk-adjusted returns (Sharpe ratio) of the portfolio by optimizing allocations to each asset class (asset allocation). The performance attributes are evaluated by comparing the portfolios both with and without Bitcoin under frameworks ranging from equal-weighted, risk-parity, and semi-constrained to unconstrained. This study suggests that Bitcoin, due to its exotic nature, unwavering appeal, and unknown set of drivers, could act as a diversifier in normal market conditions, and it might also have some borderline hedge to safe haven properties. The results further suggest that while Bitcoin may be a potential diversifier for a risk-seeking investor, the risk-averse investor must exercise caution by limiting their exposure to Bitcoin in their portfolios, as unnecessary exposure may increase the probability of losses in extreme market conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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163. Portfolio Optimization Constrained by Performance Attribution.
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Yuan Hu, Lindquist, W. Brent, and Rachev, Svetlozar T.
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PORTFOLIO management (Investments) ,CONSTRAINED optimization ,DOW Jones industrial average ,ASSET allocation ,SHARPE ratio ,PORTFOLIO performance ,STOCK prices - Abstract
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino-Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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164. Parametric Stability Analysis of Groin Vaults.
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Maia Avelino, Ricardo, Iannuzzo, Antonino, Van Mele, Tom, Block, Philippe, Cennamo, Claudia, and Cusano, Concetta
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GROIN ,FORCE density ,CONSTRAINED optimization ,GOTHIC architecture ,SAFETY factor in engineering ,NONLINEAR equations - Abstract
This paper presents a parametric stability study of groin, or cross vaults, a structural element widely used in old masonry construction, particularly in Gothic architecture. The vaults' stability is measured using the geometric safety factor (GSF), computed by evaluating the structure's minimum thickness through a thrust network analysis (TNA). This minimum thickness is obtained by formulating and solving a specific constrained nonlinear optimisation problem. The constraints of this optimisation enforce the limit analysis's admissibility criteria, and the equilibrium is calculated using independent force densities on a fixed horizontal projection of the thrust network. The parametric description of the vault's geometry is defined with respect to the radius of curvature of the vault and its springing angle. This detailed parametric study allows identifying optimal parameters which improve the vaults' stability, and a comprehensive comparison of these results was performed with known results available for two-dimensional pointed arches. Moreover, an investigation of different force flows represented by different form diagrams was performed, providing a better understanding of the vaults' structural behaviour, and possible collapse mechanisms were studied by observing the points where the thrust network touches the structural envelope in the limit states. Beyond evaluating the GSF, the groin vault's stability domain was described to give additional insights into the structural robustness. Finally, this paper shows how advances in equilibrium methods can be useful to understand and assess masonry groin vaults. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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165. The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections.
- Author
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Németh, Balázs, Gáspár, Péter, and Fortuna, Luigi
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ROAD interchanges & intersections ,AUTONOMOUS vehicles ,REINFORCEMENT learning ,CONSTRAINED optimization ,SURETYSHIP & guaranty - Abstract
The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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166. Optimization of Metal–Ceramic Functionally Graded Plates Using the Simulated Annealing Algorithm.
- Author
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Franco Correia, Victor, Moita, José S., Moleiro, Filipa, and Soares, Cristóvão M. Mota
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SIMULATED annealing ,CERAMIC metals ,CERAMIC materials ,CONSTRAINED optimization - Abstract
This work involves the design optimization of metal–ceramic through the thickness of functionally graded material (FGM) plates subjected to thermomechanical loadings. Constrained optimization was performed for minimum mass and minimum material cost of the FGM plates. The design process of FGM plate structures requires a good choice of metal and ceramic materials and the adequate definition of the components volume fractions through the thickness direction in order to accomplish a certain structural behavior, while optimizing the material costs and/or the plate mass. Here, the optimization problems are solved with the simulated annealing (SA) algorithm, not requiring the calculation of the derivatives of the objective or constraint functions. Constrained single objective optimization cases are studied, and validated with alternative solutions, considering the p-index and the FGM plate thickness as design variables. New optimization cases, involving additionally the metal and ceramic materials as design variables, are presented both for benchmark purposes and to demonstrate the suitability of the SA algorithm to solve those optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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167. An Investigation of Radial Basis Function Method for Strain Reconstruction by Energy-Resolved Neutron Imaging.
- Author
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Aggarwal, Riya, Lamichhane, Bishnu P., Meylan, Michael H., and Wensrich, Chris M.
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NEUTRONS ,ANALYTICAL solutions ,CONSTRAINED optimization - Abstract
The main objective of the current work is to determine meshless methods using the radial basis function (rbf) approach to estimate the elastic strain field from energy-resolved neutron imaging. To this end, we first discretize the longitudinal ray transformation with rbf methods to give us an unconstrained optimization problem. This discretization is then transformed into a constrained optimization problem by adding equilibrium conditions to ensure uniqueness. The efficiency and accuracy of this approach are investigated for the situation of 2d plane stress. In addition, comparisons are made between the results obtained with rbf collocation, finite-element (fem) and analytical solution methods for test problems. The method is then applied to experimentally measured continuous and discontinuous strain fields using steel samples for an offset ring-and-plug and crushed ring, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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168. Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model.
- Author
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Xu, Kai, Wang, Gang, Wang, Liquan, Yun, Feihong, Sun, Wenhao, Wang, Xiangyu, and Chen, Xi
- Subjects
KRIGING ,COMPUTATIONAL fluid dynamics ,CONSTRAINED optimization ,GLOBAL optimization ,PUMPING machinery - Abstract
Jet pump efficiency heavily relies on the geometrical parameters of the pump design and parameter global optimization in the full variable space is still a big challenge. This paper proposed a global optimization method for annular jet pump design combining computational fluid dynamics (CFD) simulation, the Kriging approximate model and experimental data. The suction angle, the flow ratio, the diffusion angle, and the area ratio are selected as the design variables for optimization. The optimal space filling design (OSF) method is used to generate sampling points from the design space of the four design variables. The optimization method solves the constrained optimization problem with a given head ratio by building the functional relationship established by the Kriging model between efficiency and design parameters, which makes the method more applicable. The design result shows that the annular jet pump efficiency is predicted well by the Kriging model; m is a key variable affecting the annular jet pump efficiency. As the area ratio m decreases, the mixing effect at the suction chamber outlet can be improved, but the frictional resistance increases. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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169. A Spring Search Algorithm Applied to Engineering Optimization Problems.
- Author
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Dehghani, Mohammad, Montazeri, Zeinab, Dhiman, Gaurav, Malik, O. P., Morales-Menendez, Ruben, Ramirez-Mendoza, Ricardo A., Dehghani, Ali, Guerrero, Josep M., and Parra-Arroyo, Lizeth
- Subjects
PARTICLE swarm optimization ,MATHEMATICAL optimization ,SEARCH engines ,ALGORITHMS ,SPRING ,SEARCH algorithms ,CONSTRAINED optimization - Abstract
At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke's law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke's law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA's usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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170. Energy Commitment for a Power System Supplied by Multiple Energy Carriers System using Following Optimization Algorithm.
- Author
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Dehghani, Mohammad, Mardaneh, Mohammad, Malik, Om Parkash, Guerrero, Josep M., Morales-Menendez, Ruben, Ramirez-Mendoza, Ricardo A., Matas, José, and Abusorrah, Abdullah
- Subjects
POWER resources ,COAL supply & demand ,MATHEMATICAL optimization ,ENERGY consumption ,CONSTRAINED optimization ,SUPPLY & demand - Abstract
In today's world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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171. A Design for a Manufacturing-Constrained Off-Axis Four-Mirror Reflective System.
- Author
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Liu, Ruoxin, Li, Zexiao, Duan, Yiting, and Fang, Fengzhou
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REFLECTIVE learning ,GENETIC algorithms ,SYSTEMS design ,CONSTRAINED optimization - Abstract
Off-axis reflective optical systems find wide applications in various industries, but the related manufacturing issues have not been well considered in the design process. This paper proposed a design method for cylindrical reflective systems considering manufacturing constraints to facilitate ultra-precision raster milling. An appropriate index to evaluate manufacturing constraints is established. The optimization solution is implemented for the objective function composed of primary aberration coefficients with weights and constraint conditions of the structural configuration by introducing the genetic algorithm. The four-mirror initial structure with a good imaging quality and a special structural configuration is then obtained. The method's feasibility is validated by designing an off-axis four-mirror afocal system with an entrance pupil diameter of 170 mm, a field of view of 3° × 3° and a compression ratio of five times. All mirrors in the system are designed to be distributed along a cylinder. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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172. Kinematic Conceptual Design of In-Line Four-Cylinder Variable Compression Ratio Engine Mechanisms Considering Vertical Second Harmonic Acceleration.
- Author
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Kwak, Seung Woo, Shim, Jae Kyung, and Mo, Young Kwang
- Subjects
CONCEPTUAL design ,CONSTRAINED optimization ,ENGINES ,PISTONS ,HARMONIC analysis (Mathematics) - Abstract
In the in-line four-cylinder engine, it is well known that the shaking force is due to the vertical second harmonic acceleration components of the pistons. This paper proposes a kinematic conceptual design method to determine the kinematic structure of a feasible in-line four-cylinder variable compression ratio (VCR) engine and its dimensions that would yield a lower vertical second harmonic acceleration at joints. Through type and dimensional synthesis, candidate VCR engine mechanisms are chosen and their dimensions satisfying design specifications are determined. Based on the analysis of the vertical second harmonic acceleration components at the joints, a feasible mechanism for an in-line four-cylinder VCR engine is selected. Then, the method finds the dimensions that yield a nearly minimized sum of the vertical second harmonic acceleration at each joint by adjusting the link lengths within specified tolerances. For validation, the result is compared with that of a constrained optimization using MATLAB. The proposed method would be useful at the conceptual design stage of multi-link multi-cylinder VCR and variable-stroke engine mechanisms where the second harmonic acceleration is an important design factor in the automotive industrial applications. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
173. Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints.
- Author
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Jin, Zhongjia, Liu, Sheng, Jin, Lincheng, Chen, Wei, and Yang, Weilin
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LINEAR matrix inequalities ,SAILING ships ,CONSTRAINED optimization ,COST functions ,DYNAMIC positioning systems ,CONTAINER ships ,MOTION - Abstract
A robust H
∞ -type state feedback model predictive control (H∞ -SFMPC) with input constraints is proposed to optimize the control performance during the ship sailing. Specifically, the approach employed in this paper is able to optimize the closed-loop performance with respect to an H∞ -type cost function which predicts the system performance based on the actual model instead of the ideal model. As a result, the effect caused by disturbances is attenuated. The state feedback control gain for the control input of the rudder-fin joint roll/yaw control system is obtained by solving a constrained convex optimization problem in terms of linear matrix inequalities. Simulations are carried out, which reveal that the proposed approach has outstanding control performance. Furthermore, it is found that the approach also has significant robustness with respect to parameter uncertainties. [ABSTRACT FROM AUTHOR]- Published
- 2020
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174. Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs.
- Author
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Shi, Kunming, Zhang, Xiangyin, and Xia, Shuang
- Subjects
FRUIT flies ,MATHEMATICAL optimization ,CONSTRAINED optimization ,NONLINEAR equations - Abstract
The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
175. Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems.
- Author
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Horng, Shih-Cheng and Lin, Shieh-Shing
- Subjects
NP-hard problems ,ELEPHANTS ,CALL centers ,COMPUTATIONAL complexity ,MATHEMATICAL equivalence ,CONSTRAINED optimization - Abstract
The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
176. Motion-Based Design of Passive Damping Systems to Reduce Wind-Induced Vibrations of Stay Cables under Uncertainty Conditions.
- Author
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Naranjo-Pérez, Javier, Jiménez-Alonso, Javier F., M. Díaz, Iván, Quaranta, Giuseppe, and Sáez, Andrés
- Subjects
PASSIVE components ,CABLE-stayed bridges ,CONSTRAINED optimization ,CABLES ,UNCERTAINTY - Abstract
Stay cables exhibit both great slenderness and low damping, which make them sensitive to resonant phenomena induced by the dynamic character of external actions. Furthermore, for these same reasons, their modal properties may vary significantly while in service due to the modification of the operational and environmental conditions. In order to cope with these two limitations, passive damping devices are usually installed at these structural systems. Robust design methods are thus mandatory in order to ensure the adequate behavior of the stay cables without compromising the budget of the passive control systems. To this end, a motion-based design method under uncertainty conditions is proposed and further implemented in this paper. In particular, the proposal focuses on the robust design of different passive damping devices when they are employed to control the response of stay cables under wind-induced vibrations. The proposed method transforms the design problem into a constrained multi-objective optimization problem, where the objective function is defined in terms of the characteristic parameters of the passive damping device, together with an inequality constraint aimed at guaranteeing the serviceability limit state of the structure. The performance of the proposed method was validated via its application to a benchmark structure with vibratory problems: The longest stay cable of the Alamillo bridge (Seville, Spain) was adopted for this purpose. Three different passive damping devices are considered herein, namely: (i) viscous; (ii) elastomeric; and (iii) frictions dampers. The results obtained by the proposed approach are analyzed and further compared with those provided by a conventional method adopted in the Standards. This comparison illustrates how the newly proposed method allows reduction of the cost of the three types of passive damping devices considered in this study without compromising the performance of the structure. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
177. Multibaseline Interferometric Phase Denoising Based on Kurtosis in the NSST Domain.
- Author
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Yanfang Liu, Shiqiang Li, and Heng Zhang
- Subjects
- *
KURTOSIS , *SYNTHETIC aperture radar , *CONSTRAINED optimization , *SIGNAL-to-noise ratio - Abstract
: Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
178. A One-Dimensional Stack Model for Redox Flow Battery Analysis and Operation.
- Author
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Barton, John L. and Brushett, Fikile R.
- Subjects
FLOW batteries ,POROUS electrodes ,CONSTRAINED optimization ,GENETIC algorithms ,OXIDATION-reduction reaction ,MATHEMATICAL optimization - Abstract
Current redox flow battery (RFB) stack models are not particularly conducive to accurate yet high-throughput studies of stack operation and design. To facilitate system-level analysis, we have developed a one-dimensional RFB stack model through the combination of a one-dimensional Newman-type cell model and a resistor-network to evaluate contributions from shunt currents within the stack. Inclusion of hydraulic losses and membrane crossover enables constrained optimization of system performance and allows users to make recommendations for operating flow rate, current densities, and cell design given a subset of electrolyte and electrode properties. Over the range of experimental conditions explored, shunt current losses remain small, but mass-transfer losses quickly become prohibitive at high current densities. Attempting to offset mass-transfer losses with high flow rates reduces system efficiency due to the increase in pressure drop through the porous electrode. The development of this stack model application, along with the availability of the source MATLAB code, allows for facile approximation of the upper limits of performance with limited empiricism. This work primarily presents a readily adaptable tool to enable researchers to perform either front-end performance estimates based on fundamental material properties or to benchmark their experimental results. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
179. POMDP-Based Throughput Maximization for Cooperative Communications Networks with Energy-Constrained Relay under Attack in the Physical Layer.
- Author
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Giang, Hoang Thi Huong, Vu-Van, Hiep, and Koo, Insoo
- Subjects
WIRELESS cooperative communication ,CONSTRAINED optimization ,MARKOV processes - Abstract
In this paper, we investigate jamming attacks in the physical layer against cooperative communications networks, where a jammer tries to block the data communication between the source and destination. An energy-constrained relay is able to assist the source to forward the data to the destination even when the jammer tries to block the direct link. Due to a limited-capacity battery of the relay, a non-radio frequency energy harvester equipped in the relay helps to prolong its operation. We propose a scheme based on a partially observable Markov decision process (POMDP) to find the optimal action for the source such that we can maximize the achievable throughput of cooperative communications networks. Under this scheme, the source dynamically selects the appropriate action mode for its transmission in order to obtain maximum throughput under the jamming attack. Simulation results verify that the proposed scheme is superior to the Myopic scheme where only current throughput is taken into account for making decisions. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
180. Methodology for Existing Railway Reconstruction with Constrained Optimization Based on Point Cloud Data.
- Author
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Li, Fei, Ren, Xiaochun, Luo, Wenbing, and Chen, Xiuwan
- Subjects
CONSTRAINED optimization ,POINT cloud ,SIGNAL denoising - Abstract
The reconstruction of an existing railway is important for railway reformation or double-track design. Obtaining the curve parameters of the railway and the location of the main stake accurately and rapidly is the key issue for existing railway reconstruction. A new method based on point cloud data is proposed in this paper. The issue of reconstruction was transformed into an optimization problem by constructing the objective function and introducing the constraint. With consideration of the slope of the curves' chord, the robust local weighted moving average method was used for de-noising. The time complexity was reduced greatly after separating the curve unit. The proposed method can obtain the coordinates of the main stake and the parameters of the railway by particle swarm optimization using a full direction search, combining the design requirements and geometric relations of the railway. Finally, some experiments on the design data and measured data were conducted to verify the validity of the proposed method. The results also show that the proposed method is very effective and useful for existing railway reconstruction. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
181. Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization
- Author
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Universidad de Alicante. Departamento de Tecnología Informática y Computación, Belazi, Akram, Migallón Gomis, Héctor, González-Sánchez, Daniel, González-García, Jorge, Jimeno-Morenilla, Antonio, Sanchez-Romero, Jose-Luis, Universidad de Alicante. Departamento de Tecnología Informática y Computación, Belazi, Akram, Migallón Gomis, Héctor, González-Sánchez, Daniel, González-García, Jorge, Jimeno-Morenilla, Antonio, and Sanchez-Romero, Jose-Luis
- Abstract
The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validate
- Published
- 2022
182. Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization.
- Author
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Gkiotsalitis, Konstantinos and Kumar, Rahul
- Subjects
BUS driving ,PUBLIC transit ,COMPUTER scheduling ,EVOLUTIONARY algorithms ,MATHEMATICAL optimization - Abstract
In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
183. A Tree Based Broadcast Scheme for (m, k)-firm Real-Time Stream in Wireless Sensor Networks.
- Author
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HoSung Park, Beom-Su Kim, Kyong Hoon Kim, Babar Shah, and Ki-Il Kim
- Subjects
- *
WIRELESS sensor networks , *NETWORK routing protocols , *WIRELESS sensor nodes , *ENERGY consumption , *CONSTRAINED optimization - Abstract
Recently, various unicast routing protocols have been proposed to deliver measured data from the sensor node to the sink node within the predetermined deadline in wireless sensor networks. In parallel with their approaches, some applications demand the specific service, which is based on broadcast to all nodes within the deadline, the feasible real-time traffic model and improvements in energy efficiency. However, current protocols based on either flooding or one-to-one unicast cannot meet the above requirements entirely. Moreover, as far as the authors know, there is no study for the real-time broadcast protocol to support the application-specific traffic model in WSN yet. Based on the above analysis, in this paper, we propose a new (m, k)-firm-based Real-time Broadcast Protocol (FRBP) by constructing a broadcast tree to satisfy the (m, k)-firm, which is applicable to the real-time model in resource-constrained WSNs. The broadcast tree in FRBP is constructed by the distance-based priority scheme, whereas energy efficiency is improved by selecting as few as nodes on a tree possible. To overcome the unstable network environment, the recovery scheme invokes rapid partial tree reconstruction in order to designate another node as the parent on a tree according to the measured (m, k)-firm real-time condition and local states monitoring. Finally, simulation results are given to demonstrate the superiority of FRBP compared to the existing schemes in terms of average deadline missing ratio, average throughput and energy consumption. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
184. Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism.
- Author
-
Połap, Dawid and Woźniak, Marcin
- Subjects
- *
MATHEMATICAL optimization , *ALGORITHMS , *POLAR bear behavior , *MATHEMATICAL models , *ENGINEERING - Abstract
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
185. An automated indoor localization system for online Bluetooth signal strength modeling using visual-inertial SLAM
- Author
-
Simon Tomažič and Igor Škrjanc
- Subjects
beacon ,model izgub na prenosni poti ,Computer science ,Real-time computing ,TP1-1185 ,02 engineering and technology ,visual-inertial SLAM ,01 natural sciences ,Biochemistry ,Article ,Analytical Chemistry ,law.invention ,Bluetooth low energy ,Bluetooth ,Data acquisition ,law ,lokalizacija v notranjem okolju ,0202 electrical engineering, electronic engineering, information engineering ,Overhead (computing) ,Path loss ,Electrical and Electronic Engineering ,Instrumentation ,particle swarm optimization ,Chemical technology ,010401 analytical chemistry ,Constrained optimization ,Particle swarm optimization ,020206 networking & telecommunications ,Radiolocation ,vizualnoinercialni SLAM ,Atomic and Molecular Physics, and Optics ,constrained optimization ,0104 chemical sciences ,Beacon ,svetilnik ,udc:681.5:004 ,indoor localization ,Bluetooth z nizko porabo energije ,optimizacija z omejitvami ,optimizacija z rojem delcev ,path loss model - Abstract
Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.
- Published
- 2022
186. An efficient stochastic constrained path planner for redundant manipulators
- Author
-
Jaime Valls Miro and Arturo Gil Aparicio
- Subjects
Mathematical optimization ,Technology ,Computer science ,QH301-705.5 ,QC1-999 ,Kinematics ,manipulator motion planning ,Measure (mathematics) ,Computer Science::Robotics ,General Materials Science ,Motion planning ,Biology (General) ,Instrumentation ,QD1-999 ,Fluid Flow and Transfer Processes ,Process Chemistry and Technology ,Physics ,General Engineering ,Constrained optimization ,Construct (python library) ,stochastic planner ,Engineering (General). Civil engineering (General) ,Computer Science Applications ,Chemistry ,Path (graph theory) ,Trajectory ,Robot ,manipulability ,TA1-2040 - Abstract
This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks.
- Published
- 2021
187. Adaptive Waveform Design with Multipath Exploitation Radar in Heterogeneous Environments
- Author
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Ullo, Silvia Liberata, Zarro, Chiara, Yılmaz, Seden Hazal Gülen, Hayvacı, Harun Taha, Ullo, Silvia Liberata, Zarro, Chiara, Yılmaz, Seden Hazal Gülen, and Hayvacı, Harun Taha
- Abstract
The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from the adjacent cells. The proposed GLRT is also used at the end of the second transmission for the final decision. Extensive performance evaluation of the proposed detector and adaptive waveform for various multipath scenarios is presented. The performance analysis prove that the proposed method improves the Signal-to-Clutter Ratio (SCR) of the received signal, and the detection performance with multipath exploitation.
- Published
- 2021
188. State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints
- Author
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Mousa Marzband, Fernando V. Cerna, Matti Lehtonen, Mahdi Pourakbari-Kasmaei, Ehsan Naderi, and Hossein Narimani
- Subjects
Mathematical optimization ,Optimization problem ,cybersecurity ,Heuristic (computer science) ,Computer science ,H600 ,020209 energy ,Bioengineering ,02 engineering and technology ,TP1-1185 ,meta-heuristic algorithms ,Electric power system ,0202 electrical engineering, electronic engineering, information engineering ,Chemical Engineering (miscellaneous) ,QD1-999 ,constraint optimization ,Process Chemistry and Technology ,Chemical technology ,020208 electrical & electronic engineering ,Constrained optimization ,Solver ,AC power ,Power (physics) ,Chemistry ,Flow (mathematics) ,heuristic algorithms ,optimization methods ,optimal power flow (OPF) - Abstract
Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.
- Published
- 2021
189. On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes.
- Author
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Binette, Jean-Christophe and Srinivasan, Bala
- Subjects
NONLINEAR statistical models ,PREDICTIVE control systems ,BATCH processing ,COST functions ,MANUFACTURING processes - Abstract
Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
190. On the Deployment of a Connected Sensor Network for Confident Information Coverage.
- Author
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Huping Xu, Jiajun Zhu, and Bang Wang
- Subjects
- *
WIRELESS sensor networks , *WIRELESS communications , *SENSOR networks , *CONSTRAINED optimization , *HEURISTIC algorithms , *SIMULATION methods & models - Abstract
Coverage and connectivity are two important performance metrics in wireless sensor networks. In this paper, we study the sensor placement problem to achieve both coverage and connectivity. Instead of using the simplistic disk coverage model, we use our recently proposed confident information coverage model as the sensor coverage model. The grid approach is applied to discretize the sensing field, and our objective is to place the minimum number of sensors to form a connected network and to provide confident information coverage for all of the grid points. We first formulate the sensor placement problem as a constrained optimization problem. Then, two heuristic algorithms, namely the connected cover formation (CCF) algorithm and the cover formation and relay placement with redundancy removal (CFRP-RR) algorithm, are proposed to find the approximate solutions for the sensor placement problem. The simulation results validate their effectiveness, and the CCF algorithm performs slightly better than the CFRP-RR algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
191. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty.
- Author
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Bo Chen, Fang Qiu, Bingfang Wu, and Hongyue Du
- Subjects
- *
IMAGE segmentation , *IMAGE analysis , *MULTISCALE modeling , *REMOTE sensing , *CONSTRAINED optimization - Abstract
Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition's multi-scale segmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
192. Lagrangian Submanifolds of Symplectic Structures Induced by Divergence Functions
- Author
-
Marco Favretti
- Subjects
Pure mathematics ,geometric phase transitions ,General Physics and Astronomy ,lcsh:Astrophysics ,01 natural sciences ,Article ,010305 fluids & plasmas ,canonical divergence ,0103 physical sciences ,lcsh:QB460-466 ,Morse family ,Information geometry ,0101 mathematics ,Divergence (statistics) ,lcsh:Science ,Mathematics::Symplectic Geometry ,Mathematics ,Principle of maximum entropy ,010102 general mathematics ,Lagrangian submanifolds ,Manifold ,lcsh:QC1-999 ,constrained optimization ,Connection (mathematics) ,Metric (mathematics) ,Probability distribution ,lcsh:Q ,Mathematics::Differential Geometry ,lcsh:Physics ,Symplectic geometry - Abstract
Divergence functions play a relevant role in Information Geometry as they allow for the introduction of a Riemannian metric and a dual connection structure on a finite dimensional manifold of probability distributions. They also allow to define, in a canonical way, a symplectic structure on the square of the above manifold of probability distributions, a property that has received less attention in the literature until recent contributions. In this paper, we hint at a possible application: we study Lagrangian submanifolds of this symplectic structure and show that they are useful for describing the manifold of solutions of the Maximum Entropy principle.
- Published
- 2020
193. Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method.
- Author
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Liu, Qingqing, Cui, Caixia, and Fan, Qinqin
- Subjects
- *
DIFFERENTIAL evolution , *ALGORITHMS , *CONSTRAINED optimization , *EVOLUTIONARY computation , *REINFORCEMENT learning - Abstract
The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
194. Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization.
- Author
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Qu, Ji-Qing, Xu, Qi-Lin, and Sun, Ke-Xue
- Subjects
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GENETIC algorithms , *CONSTRAINED optimization , *LIGHTING designers , *SIMULATION software , *MATHEMATICAL models - Abstract
An improved mathematical model and an improved particle swarm optimization (IPSO) are proposed for the complex design parameters and conflicting design goals of the indoor luminaire layout (ILL) problem. The ILL problem is formulated as a nonlinear constrained mixed-variable optimization problem that has four decision variables. For a general lighting scheme (GLS), the number and location of luminaires can be uniquely determined by optimizing four decision variables, which avoid using program loops to determine the number of luminaires. We improve the particle swarm optimization (PSO) in three aspects: (1) up-down probabilistic rounding (UDPR) method proposed to solve mixed integer, (2) improving the velocity of the best global particle, and (3) using nonlinear inertia weights with random items. The IPSO has better optimization results in an office study compared with the PSO and genetic algorithm (GA). The results are validated by DIALux simulation software, and a maximum deviation of 2.2% is found. The validated results show that the method using four decision variables increased the speed by 10.6% and the success rate by 23.33%. Furthermore, Indoor Luminaire Layout System APP is designed to provide guidelines visually for lighting designers and related researchers. [ABSTRACT FROM AUTHOR]
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- 2022
- Full Text
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195. Best-Practice Aspects of Quantum-Computer Calculations: A Case Study of the Hydrogen Molecule.
- Author
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Miháliková, Ivana, Friák, Martin, Pivoluska, Matej, Plesch, Martin, Saip, Martin, and Šob, Mojmír
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QUANTUM computers , *STOCHASTIC approximation , *HYDROGEN as fuel , *QUANTUM chemistry , *CONSTRAINED optimization , *QUBITS - Abstract
Quantum computers are reaching one crucial milestone after another. Motivated by their progress in quantum chemistry, we performed an extensive series of simulations of quantum-computer runs that were aimed at inspecting the best-practice aspects of these calculations. In order to compare the performance of different setups, the ground-state energy of the hydrogen molecule was chosen as a benchmark for which the exact solution exists in the literature. Applying the variational quantum eigensolver (VQE) to a qubit Hamiltonian obtained by the Bravyi–Kitaev transformation, we analyzed the impact of various computational technicalities. These included (i) the choice of the optimization methods, (ii) the architecture of the quantum circuits, as well as (iii) the different types of noise when simulating real quantum processors. On these, we eventually performed a series of experimental runs as a complement to our simulations. The simultaneous perturbation stochastic approximation (SPSA) and constrained optimization by linear approximation (COBYLA) optimization methods clearly outperformed the Nelder–Mead and Powell methods. The results obtained when using the R y variational form were better than those obtained when the R y R z form was used. The choice of an optimum entangling layer was sensitively interlinked with the choice of the optimization method. The circular entangling layer was found to worsen the performance of the COBYLA method, while the full-entangling layer improved it. All four optimization methods sometimes led to an energy that corresponded to an excited state rather than the ground state. We also show that a similarity analysis of measured probabilities can provide a useful insight. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
196. A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations.
- Author
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Pellegrini, Riccardo, Serani, Andrea, Liuzzi, Giampaolo, Rinaldi, Francesco, Lucidi, Stefano, and Diez, Matteo
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POTENTIAL flow , *NONSMOOTH optimization , *CONSTRAINED optimization , *DESIGN exhibitions , *ALGORITHMS - Abstract
The paper presents a multi-fidelity extension of a local line-search-based derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for use in the simulation-driven design optimization (SDDO) context, where multi-fidelity computations are used to evaluate the objective function. The proposed algorithm starts using low-fidelity evaluations and automatically switches to higher-fidelity evaluations based on the line-search step length. The multi-fidelity algorithm is driven by a suitably defined threshold and initialization values for the step length, which are associated to each fidelity level. These are selected to increase the accuracy of the objective evaluations while progressing to the optimal solution. The method is demonstrated for a multi-fidelity SDDO benchmark, namely pertaining to the hull-form optimization of a destroyer-type vessel, aiming at resistance minimization in calm water at fixed speed. Numerical simulations are based on a linear potential flow solver, where seven fidelity levels are used selecting systematically refined computational grids for the hull and the free surface. The method performance is assessed varying the steplength threshold and initialization approach. Specifically, four MF-CS-DFN setups are tested, and the optimization results are compared to its single-fidelity (high-fidelity-based) counterpart (CS-DFN). The MF-CS-DFN results are promising, achieving a resistance reduction of about 12% and showing a faster convergence than CS-DFN. Specifically, the MF extension is between one and two orders of magnitude faster than the original single-fidelity algorithm. For low computational budgets, MF-CS-DFN optimized designs exhibit a resistance that is about 6% lower than that achieved by CS-DFN. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
197. Modeling and Design Optimization of Energy Transfer Rate for Hybrid Energy Storage System in Electromagnetic Launch.
- Author
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Ma, Tao, Lu, Junyong, Zhang, Xiao, Zhu, Bofeng, Wu, Wenxuan, and Long, Xinlin
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ENERGY storage , *ENERGY transfer , *CONSTRAINED optimization , *CAPACITORS - Abstract
The battery-pulse capacitor-based hybrid energy storage system has the advantage of high-energy density and high-power density. However, to achieve a higher firing rate of the electromagnetic launch, a shorter charging time of the pulse capacitor from the battery is needed. A new optimization model by formulating the charging time problem as a constrained optimization problem is presented. Unlike existing algorithms, the proposed model can find the globally optimal solution. The circuit parameters are optimized through the Enumeration algorithm to minimize the total charging time of the pulse capacitors from batteries. The simulation results show that the charging time of the proposed algorithm is shorter than the compared methods. Furthermore, a better solution could be obtained by canceling the constraint on the first peak of the charging current of the compared methods, which makes the circuit design more flexible for the hybrid energy storage system in the electromagnetic launch. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
198. Servo Robust Control of Uncertain Mechanical Systems: Application in a Compressor/PMSM System.
- Author
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Zhang, Qiang, Yu, Rongrong, Li, Chenming, Chen, Ye-Hwa, and Gu, Jieying
- Subjects
ROBUST control ,UNCERTAIN systems ,PERMANENT magnet motors ,COMPRESSORS ,CONSTRAINED optimization - Abstract
High-speed Permanent Magnet Synchronous Motor (PMSM) systems have been widely used in industry and other fields for their advantages of having a simple structure, low processing cost and high efficiency. At present, the control precision of PMSM is required to be higher and higher, but it faces two major challenges. The first is that the PMSM system possesses (possibly fast) time-varying uncertainty. The second is that there exist nonlinear portions in the PMSM system, such as nonlinear elasticity, etc. To resolve these challenges, a novel performance measure β ^ is introduced as a dynamic depiction of the constraint-following error, and a new robust control design is proposed based on β ^ . While this control renders guaranteed performance regardless of uncertainty, an optimal design of a control parameter is further pursued. This inquiry is summed up as a semi-infinite constrained optimization problem. After the induction of the necessary condition, the candidate solutions can be identified. These are further screened by a sufficient condition, which results in the actual solution. To verify the effectiveness of the control design, the compressor powered by a super high-speed PMSM system is simulated, and its performance is discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
199. A Relaxed and Bound Algorithm Based on Auxiliary Variables for Quadratically Constrained Quadratic Programming Problem.
- Author
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Hu, Chenyang, Gao, Yuelin, Tian, Fuping, and Ma, Suxia
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CONSTRAINED optimization , *ALGORITHMS , *QUADRATIC programming , *ENGINEERING management , *MANAGEMENT science , *INDUSTRIAL engineering , *RELAXATION techniques - Abstract
Quadratically constrained quadratic programs (QCQP), which often appear in engineering practice and management science, and other fields, are investigated in this paper. By introducing appropriate auxiliary variables, QCQP can be transformed into its equivalent problem (EP) with non-linear equality constraints. After these equality constraints are relaxed, a series of linear relaxation subproblems with auxiliary variables and bound constraints are generated, which can determine the effective lower bound of the global optimal value of QCQP. To enhance the compactness of sub-rectangles and improve the ability to remove sub-rectangles, two rectangle-reduction strategies are employed. Besides, two ϵ -subproblem deletion rules are introduced to improve the convergence speed of the algorithm. Therefore, a relaxation and bound algorithm based on auxiliary variables are proposed to solve QCQP. Numerical experiments show that this algorithm is effective and feasible. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
200. New Algorithm to Solve Mixed Integer Quadratically Constrained Quadratic Programming Problems Using Piecewise Linear Approximation.
- Author
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Alkhalifa, Loay and Mittelmann, Hans
- Subjects
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PIECEWISE linear approximation , *QUADRATIC programming , *MIXED integer linear programming , *CONSTRAINED optimization , *NONLINEAR programming , *ALGORITHMS - Abstract
Techniques and methods of linear optimization underwent a significant improvement in the 20th century which led to the development of reliable mixed integer linear programming (MILP) solvers. It would be useful if these solvers could handle mixed integer nonlinear programming (MINLP) problems. Piecewise linear approximation (PLA) is one of most popular methods used to transform nonlinear problems into linear ones. This paper will introduce PLA with brief a background and literature review, followed by describing our contribution before presenting the results of computational experiments and our findings. The goals of this paper are (a) improving PLA models by using nonuniform domain partitioning, and (b) proposing an idea of applying PLA partially on MINLP problems, making them easier to handle. The computational experiments were done using quadratically constrained quadratic programming (QCQP) and MIQCQP and they showed that problems under PLA with nonuniform partition resulted in more accurate solutions and required less time compared to PLA with uniform partition. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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